1. Technical Field
The present invention relates to speech or voice recognition systems, and more particularly to speech recognition systems for use in voice processing systems and the like.
2. Description of the Related Art
Voice processing systems whereby callers interact over the telephone network with computerized equipment are very well-known in the art, and include voice mail systems, voice response units, and so on. Typically such systems ask a caller (or called party) questions using prerecorded prompts, and the caller inputs answers by pressing dual tone multiple frequency (DTMF) keys on their telephones. This approach has proven effective for simple interactions, but is clearly restricted in scope due to the limited number of available keys on a telephone. For example, alphabetical input is particularly difficult using DTMF keys.
There has therefore been an increasing tendency in recent years for voice processing systems to use voice recognition in order to augment DTMF input. The adoption of voice recognition permits the handling of callers who do not have a DTMF phone, and also the acquisition of more complex information beyond simple numerals from the caller.
As an illustration of the above, W096/25733 describes a voice response system which includes a prompt unit, a Voice Activity Detector (VAD), and a voice recognition unit. In this system, as a prompt is played to the caller, any input from the caller is passed to the VAD, together with the output from the prompt unit. This allows the VAD to perform echo cancellation on the incoming signal. Then, in response to the detection of voice by the VAD, the prompt is discontinued, and the caller input is switched to the recognition unit, thereby providing a barge-in facility.
Voice recognition in a telephony environment can be supported by a variety of hardware architectures. Many voice processing systems include a special DSP card for running voice recognition software. This card is connected to a line interface unit for the transfer of telephony data by a time division multiplex (TDM) bus. Most commercial voice processing systems, more particularly their line interface units and DSP cards, conform to one of two standard architectures: either the Signal Computing System Architecture (SCSA), or the Multi-vendor Integration Protocol (MVIP). A somewhat different configuration is described in GB 2280820, in which a voice processing system is connected via a local area network to a remote server, which provides a voice recognition facility. This approach is somewhat more complex than the TDM approach, given the data communication and management required, but does offer significantly increased flexibility.
Speech recognition systems are generally used in telephony environments as cost-effective substitutes for human agents, and are adequate for performing simple, routine tasks. It is important that such tasks are performed accurately otherwise there may be significant customer dissatisfaction, and also as quickly as possible, both to improve caller throughput, and also because the owner of the voice processing system is often paying for the call via some FreePhone mechanism (e.g., an 800 number).
Speech recognition systems are most successful in environments where voice input is restricted to a small and limited vocabulary. Call centers, for example, typically prompt for single digit input in order to route their customers to the appropriate department. I.e. xe2x80x9cPlease say One for Technical Support, Two for Sales, Three for Customer Servicesxe2x80x9d and so on. Here, the customer must respond with one of three choices and thus the margin for error is greatly reduced.
With continuing improvements in recognition accuracy, however, the large vocabulary speech recognition systems which have been developed are starting to be used in more and more complex situations, which have hitherto been the exclusive realm of human operators. Nevertheless, even with their impressive ability to recognize speech, such systems are still deficient at providing as complete a service to the caller as a human agent could manage.
The recognition of proper names, surnames, and place names, which are often outside the recognition system""s dictionary still prove a significant challenge for such systems. Unusual or varied pronunciations further exacerbate the problem. Speech recognition systems may, for example, typically be required to recognize a customer""s first and surnames and to take down their address correctly. It is just not possible for these systems to cater for the wide variety of responses which they may encounter when requesting such information.
One possibility is to ask a caller to spell any unrecognized words. A person living in xe2x80x9cHarestockxe2x80x9d, for example might be asked to spell out H A R E S T O C K. Unfortunately, this solution in itself has its problems. Many of letters in the alphabet have very similar pronunciations. S and F; B and P; and M and N are just a few examples of those which may easily be confused. Indeed this difficulty applies to both humans and speech recognition systems.
The need to recognize alphabetic letters occurs not only in the spelling of words which cause problems, but also single/sequences of alphabetic character(s) when the caller is asked to give information such as car registration numbers, catalogue references, etc. It may be difficult to distinguish, for example, whether a car registration is actually M799 ABM or N799 APN. Incidentally, numeric digits prove far easier to identify than alphabetic characters since there are fewer possibilities and they are acoustically more distinct.
It is known in certain environments (e.g., radio communications) to try to avoid such confusion by using the International Civil Aviation Organization Phonetic Alphabet (ICAO), whereby alphabetic characters are associated with certain words: A for Alpha, C for Charlie, T for Tango, etc. In this case, each letter can be recognized simply by listening to its corresponding word. However, this approach is difficult for commercial speech recognition systems since the general public will often not know the ICAO. Furthermore, this is not the only phonetic alphabet in existence. For instance, there are three different versions in use in the United States. Someone in the military a number of years ago, for example, might use xe2x80x9cA for Ablexe2x80x9d rather than xe2x80x9cA for Alphaxe2x80x9d.
Accordingly, the invention provides a method of performing speech recognition to determine a particular alphabetic character, comprising the steps of: a) receiving acoustic spoken input comprising a single alphabetic character and a word associated with the single character such that; the first character of said word is intended to be the same as said single alphabetic character; b) processing said acoustic input by using a large speech vocabulary recognition system to recognize said single alphabetic character and said word; c) determining the first character of said recognized word; d) comparing the recognized single alphabetic character with the determined first character of said recognized word; and e) responsive to said recognized single alphabetic character being the same as said first character of the recognized word, accepting said character as the determined alphabetic character for the spoken input.
Such a method finds particular applicability when prompting for alphanumerics (e.g., car registration numbers, catalogue references, etc). In this situation, the system is only required to recognize a discrete set of letters (i.e. from a set of twenty-six), but similarities in sound between some characters may cause difficulty. However, rather than seeking to improve the recognition performance per se of a discrete word recognition system, the invention adopts a different strategy. Thus by using a large vocabulary recognition system and associating a word with an alphabetic character, this difficulty is overcome. The large vocabulary recognition system allows the word to be an essentially arbitrary one so there is no reliance upon a user having familiarity with a particular phonetic alphabet. Although it is more expensive to use a large vocabulary system for dealing with a discrete set of letters, this is compensated for by an improved quality of speech recognition.
In the preferred embodiment both the single alphabetic character and the first character of the word must be recognized and must match one another for the alphabetic character of the spoken input to be determined. This approach offers improved recognition by using both elements to ensure that the correct alphabetic character is accepted, thereby providing an efficient method of error checking. If the single alphabetic character does not match the first character of the recognized word, the system can take appropriate action, such as re-prompting for further input.
An alternative embodiment may accept the single alphabetic character even if the word has not been recognized. Conversely, if only the word is recognized, the system may just use the first character of the word and accept this. However, this approach is more prone to inaccuracies since there is no error checking involved. A slight improvement on this can be achieved by the system maintaining a list of alphabetic characters which are not easily confused (e.g., K, X, Y, and Z) and if only the single alphabetic character is recognized and this appears on the list, then the system uses this character to determine the alphabetic character for the spoken input. Obviously the converse could be usedxe2x80x94i.e. the system maintains a list of ambiguous alphabetic characters and does not accept the single alphabetic character as the spoken input if it appears on this list. This approach is more robust and avoids re-prompting for spoken input when the character initially input is distinct enough to be reliably recognized by itself.
Another possibility is for the system to store a history of successive unsuccessful matches and in this way it can identify any repeated inconsistencies and make an educated guess. If, for example, the first letter of the word is consistently the same, then it may assume that it is mistaking the single alphabetic character and choose to disregard this element. This again avoids unnecessary re-prompting and allows the system to use previously received information in order to make an intelligent decision.
Rather than accepting input in the form xe2x80x9cletter for wordxe2x80x9d, the system may accept a word only. In this situation the first character of the word denotes the alphabetic character being spelled out. This approach however is not particularly natural for the user and does not provide any form of error checking.
In the preferred embodiment, the method is implemented using a speech recognition system which is incorporated into a voice processing system connected to a telephone network. The spoken input is received from a caller over this network. Typically whole alphanumeric strings are received, spelt out character by character. Audible notification (e.g., a beep) indicates the successful recognition of each character and once the whole string has been spelt out, it can be played back for confirmation. Alternatively, each character may be played back and confirmation requested before receiving the next character string. Such confirmation may, for example, be indicated by the caller pressing a DTMF key or providing some form of audible acceptance. Another possibility is for confirmation to be indirect, i.e., assumed when the next character in the string is received as input rather than some DTMF key. Either approach provides an additional check to ensure that the system is accepting the correct information.
In a further aspect the invention provides apparatus for performing speech recognition to determine a particular alphabetic character, comprising: a) means for receiving acoustic spoken input comprising a single alphabetic character and a word associated with the single character such that the first character of said word is intended to be the same as said single alphabetic character; b) a large speech vocabulary recognition system for processing said acoustic input for recognizing said single alphabetic character and said word; c) means for determining the first character of said recognized word; d) means for comparing the recognized single alphabetic character with the determined first character of said recognized word; and e) means, responsive to said recognized single alphabetic character being the same as said first character of the recognized word, for accepting said character as the determined alphabetic character for the spoken input.
In a yet still further aspect, the invention provides a computer program product comprising program code stored on a computer readable storage medium for, when executed on a computer, performing speech recognition to determine a particular alphabetic character, by executing the steps of: a) receiving acoustic spoken input comprising a single alphabetic character and a word associated with the single character such that the first character of said word is intended to be the same as said single alphabetic character; b) processing said acoustic input by using a large speech vocabulary recognition system to recognize said single alphabetic character and said word; c) determining the first character of said recognized word; d) comparing the recognized single alphabetic character with the determined first character of said recognized word; and e) responsive to said recognized single alphabetic character being the same as said first character of the recognized word, accepting said character as the determined alphabetic character for the spoken input.